Predictive Modeling Applications in Actuarial Science
- Volume 1
- Introduction
- Predictive Modeling Foundations
- Predictive Modeling Methods
- Bayesian and Mixed Modeling
- Longitudinal Modeling
- Volume 2
- Generalized Linear Model
- Extensions of the Generalized Linear Model
- Unsupervised Predictive Modeling Methods
-
Applications on Current Problems in Actuarial Science
- Chapter 8 - The Predictive Distribution of Loss Reserve Estimates over a Finite Time Horizon
- Chapter 9 - Finite Mixture Model and Workers’ Compensation Large-Loss Regression Analysis
- Chapter 10 - A Framework for Managing Claim Escalation Using Predictive Modeling
- Chapter 11 - Predictive Modeling for Usage-Based Auto Insurance
Chapter 6 - Frequency and Severity Models
Authors
Edward W. Frees | University of Wisconsin-Madison
jfrees@bus.wisc.edu
Chapter Preview
Many insurance data sets feature information about how often claims arise, the frequency, in addition to the claim size, the severity. This chapter introduces tools for handling the joint distribution of frequency and severity. Frequency-severity modeling is important in insurance applications because of features of contracts, policyholder behavior, databases that insurers maintain, and regulatory requirements. Model selection depends on the data form. For some data, we observe the claim amount and think about a zero claim as meaning no claim during that period. For other data, we observe individual claim amounts. Model selection also depends upon the purpose of the inference; this chapter highlights the Tweedie generalized linear model as a desirable option. To emphasize practical applications, this chapter features a case study of Massachusetts automobile claims, using out-of-sample validation for model comparisons.
| Data | R Demonstrations | R Code |
| Generalized Linear Model: Example | Generalized Linear Model: Example | |
| Grouped Versus Individual Data Example | Grouped Versus Individual Data Example | |
| Massachusetts Automobile Example | ||
| Insample Data | Summary Statistics | Summary Statistics |
| OutSample Data | Model Fitting | Model Fitting |
| Out-of-Sample Validation | Out-of-Sample Validation |